{"id":"W3089923376","doi":"10.1109/icra40945.2020.9197217","title":"AC/DCC : Accurate Calibration of Dynamic Camera Clusters for Visual SLAM","year":2020,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Calibration; Gimbal; Computer science; Artificial intelligence; Computer vision; Noise (video); Camera auto-calibration; Projection (relational algebra); Collinearity; Fiducial marker; Camera resectioning; Joint (building); Algorithm; Mathematics; Engineering; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002512212,0.0000768728,0.0001069556,0.00002731257,0.00001856945,0.0000172269,0.00004532405,0.00004695161,0.00002926511],"category_scores_gemma":[0.00002041282,0.0000743944,0.00003893621,0.00008432511,0.000009205609,0.00009068177,0.000007438362,0.0000337101,0.00000314171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001754798,"about_ca_system_score_gemma":0.00001007699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008704219,"about_ca_topic_score_gemma":0.00001745111,"domain_scores_codex":[0.9995557,0.000007573335,0.0001803179,0.00008906249,0.00006723595,0.000100099],"domain_scores_gemma":[0.9998135,0.00002956287,0.00002310366,0.0000548004,0.00003265846,0.00004637139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001383653,0.000006287255,0.00001990836,0.0001082373,0.00001781854,3.275804e-7,0.0002146593,0.9805675,0.01596204,0.0005806301,0.0008204111,0.001688361],"study_design_scores_gemma":[0.0002610438,0.00006588482,0.0000461995,0.000005888351,0.00001124497,1.888376e-7,0.00006713618,0.9904549,0.008739698,0.00003142259,0.0002296769,0.00008668617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04718076,0.00001589866,0.9515865,0.0004807264,0.0001096668,0.0001892078,0.00001011396,0.0001261633,0.000300972],"genre_scores_gemma":[0.9963695,0.0000125412,0.003174223,0.0002378869,0.00003387371,0.000005664744,0.00008373604,0.00002335962,0.00005919394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9491888,"threshold_uncertainty_score":0.3033716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01351805737665811,"score_gpt":0.2417081385376863,"score_spread":0.2281900811610282,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}